Performance Analysis of Cooperative Spectrum Sensing in Cognitive Vehicular Networks with Dense Traffic

Abstract

Cognitive Radio (CR) is a promising technology to solve the spectrum scarcity. Spectrum sensing becomes more challenging in Cognitive Vehicular Networks (CVNs) due to Secondary Users (SUs) mobility. Current studies on cooperative spectrum sensing usually assume that sensors are static and independent, which is unreasonable in vehicular networks with dense traffic. In this paper, we investigate the cooperative spectrum sensing performance in dense traffic with the presence of SUs mobility and correlation. First of all, we establish a mobility model in dense traffic by analyzing the trajectory data provided by Next Generation SIMulation (NGSIM) program. Secondly, detection probability and false alarm probability are investigated with mobile SUs and spatial-temporal spectrum opportunities. Next, mobility-driven sensing capacity is proposed to evaluate the sensing capacity available for mobile SUs. Note that SU mobility increases the sensing performance by providing spatial diversity and also enables SUs to achieve higher sensing capacity because of the presence of spatial spectrum opportunities. We also indicate that in dense networks, correlation is a crucial factor that may affect the network performance. In addition, the effects of protection range and primary user activity on spectrum sensing are studied. The theoretical analysis is further validated through simulations.